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SADM
2011
13 years 2 months ago
Random survival forests for high-dimensional data
: Minimal depth is a dimensionless order statistic that measures the predictiveness of a variable in a survival tree. It can be used to select variables in high-dimensional problem...
Hemant Ishwaran, Udaya B. Kogalur, Xi Chen, Andy J...
ICIP
2010
IEEE
13 years 5 months ago
Building Emerging Pattern (EP) Random forest for recognition
The Random forest classifier comes to be the working horse for visual recognition community. It predicts the class label of an input data by aggregating the votes of multiple tree...
Liang Wang, Yizhou Wang, Debin Zhao
KDD
2002
ACM
144views Data Mining» more  KDD 2002»
14 years 7 months ago
Efficiently mining frequent trees in a forest
Mining frequent trees is very useful in domains like bioinformatics, web mining, mining semi-structured data, and so on. We formulate the problem of mining (embedded) subtrees in ...
Mohammed Javeed Zaki
ICDM
2009
IEEE
124views Data Mining» more  ICDM 2009»
14 years 2 months ago
Rule Ensembles for Multi-target Regression
—Methods for learning decision rules are being successfully applied to many problem domains, especially where understanding and interpretation of the learned model is necessary. ...
Timo Aho, Bernard Zenko, Saso Dzeroski
DLT
2009
13 years 5 months ago
Factorization Forests
A survey of applications of factorization forests. Fix a regular language L A . You are given a word a1
Mikolaj Bojanczyk